Binding of progastrin fragments to the 78 kDa gastrin-binding protein

AbstractThe non-selective gastrin/cholecystokinin receptor antagonists proglumide and benzotript inhibit colon carcinoma cell proliferation by binding to the 78 kDa gastrin-binding protein (GBP) (Baldwin, Proc. Natl. Acad. Sci. USA, 91 (1994) 7593–7597). However, although most colon carcinoma cell lines synthesize progastrin, production of mature amidated gastrin17 has not been observed. In order to define the structural requirements for the binding of gastrin to the GBP the affinities of various fragments of amidated and C-terminally extended gastrin17 for the GBP have been measured. The results indicate that the GBP recognizes both N- and C-termini of gastrin17. Moreover since C-terminal amidation is not a prerequisite for binding of gastrin to the GBP, the GBP is a potential target for the autocrine effects of progastrin

p21-activated kinases and gastrointestinal cancer

Abstractp21-activated kinases (PAKs) were initially identified as effector proteins downstream from GTPases of the Rho family. To date, six members of the PAK family have been discovered in mammalian cells. PAKs play important roles in growth factor signalling, cytoskeletal remodelling, gene transcription, cell proliferation and oncogenic transformation. A large body of research has demonstrated that PAKs are up-regulated in several human cancers, and that their overexpression is linked to tumour progression and resistance to therapy. Structural and biochemical studies have revealed the mechanisms involved in PAK signalling, and opened the way to the development of PAK-targeted therapies for cancer treatment. Here we summarise recent findings from biological and clinical research on the role of PAKs in gastrointestinal cancer, and discuss the current status of PAK-targeted anticancer therapies

Glaucarubinone inhibits colorectal cancer growth by suppression of hypoxia-inducible factor 1α and β-catenin via a p-21 activated kinase 1-dependent pathway

Abstractp-21-Activated kinase 1 (PAK1) enhances colorectal cancer (CRC) progression by stimulating Wnt/β-catenin, ERK and AKT pathways. PAK1 also promotes CRC survival via up-regulation of hypoxia-inducible factor 1α (HIF-1α), a key player in cancer survival. Glaucarubinone, a quassinoid natural product, inhibits pancreatic cancer growth by down-regulation of PAK1. The aim of this study was to investigate the effect of glaucarubinone on CRC growth and metastasis, and the mechanism involved. Cell proliferation was measured in vitro by [3H]-thymidine incorporation and in vivo by volume of tumor xenografts. Protein concentrations were measured by Western blotting of cell extracts. We report here that glaucarubinone inhibited CRC growth both in vitro and in vivo. The potency of glaucarubinone as an inhibitor of cell proliferation was negatively correlated to PAK1 expression in CRC cells. Glaucarubinone suppressed the expression of HIF-1α and β-catenin. Knockdown of PAK1 by shRNA enhanced inhibition by glaucarubinone while constitutively active PAK1 blocked the inhibitory effect. Our findings indicate that glaucarubinone inhibited CRC growth by down-regulation of HIF-1α and β-catenin via a PAK1-dependent pathway

Circulating gastrin is increased in hemochromatosis

AbstractGastric acid production is important in intestinal iron absorption. The peptide hormone gastrin exists in both amidated and non-amidated forms, which stimulate and potentiate gastric acid secretion, respectively. Since non-amidated gastrins require ferric ions for biological activity in vitro, this study investigated the connection between iron status and gastrin by measurement of circulating gastrin concentrations in mice and humans with hemochromatosis. Gastrin concentrations are increased in the plasma and gastric mucosa of Hfe−/− mice, and in the sera of humans with HFE-related hemochromatosis. The discovery of a relationship between iron status and circulating gastrin concentrations opens a new perspective on the mechanisms of iron homeostasis

Testing a word is not a test of word learning

Although vocabulary acquisition requires children learn names for multiple things, many investigations of word learning mechanisms teach children the name for only one of the objects presented. This is problematic because it is unclear whether children's performance reflects recall of the correct name-object association or simply selection of the only object that was singled out by being the only object named. Children introduced to one novel name may perform at ceiling as they are not required to discriminate on the basis of the name per se, and appear to rapidly learn words following minimal exposure to a single word. We introduced children to four novel objects. For half the children, only one of the objects was named and for the other children, all four objects were named. Only children introduced to one word reliably selected the target object at test. This demonstration highlights the over-simplicity of one-word learning paradigms and the need for a shift in word learning paradigms where more than one word is taught to ensure children disambiguate objects on the basis of their names rather than their degree of salience

Predicting dry matter intake in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks.

Dry matter intake (DMI) is a fundamental component of the animal's feed efficiency, but measuring DMI of individual cows is expensive. Mid-infrared reflectance spectroscopy (MIRS) on milk samples could be an inexpensive alternative to predict DMI. The objectives of this study were (1) to assess if milk MIRS data could improve DMI predictions of Canadian Holstein cows using artificial neural networks (ANN); (2) to investigate the ability of different ANN architectures to predict unobserved DMI; and (3) to validate the robustness of developed prediction models. A total of 7,398 milk samples from 509 dairy cows distributed over Canada, Denmark, and the United States were analyzed. Data from Denmark and the United States were used to increase the training data size and variability to improve the generalization of the prediction models over the lactation. For each milk spectra record, the corresponding weekly average DMI (kg/d), test-day milk yield (MY, kg/d), fat yield (FY, g/d), and protein yield (PY, g/d), metabolic body weight (MBW), age at calving, year of calving, season of calving, days in milk, lactation number, country, and herd were available. The weekly average DMI was predicted with various ANN architectures using 7 predictor sets, which were created by different combinations MY, FY, PY, MBW, and MIRS data. All predictor sets also included age of calving and days in milk. In addition, the classification effects of season of calving, country, and lactation number were included in all models. The explored ANN architectures consisted of 3 training algorithms (Bayesian regularization, Levenberg-Marquardt, and scaled conjugate gradient), 2 types of activation functions (hyperbolic tangent and linear), and from 1 to 10 neurons in hidden layers). In addition, partial least squares regression was also applied to predict the DMI. Models were compared using cross-validation based on leaving out 10% of records (validation A) and leaving out 10% of cows (validation B). Superior fitting statistics of models comprising MIRS information compared with the models fitting milk, fat and protein yields suggest that other unknown milk components may help explain variation in weekly average DMI. For instance, using MY, FY, PY, and MBW as predictor variables produced a predictive accuracy (r) ranging from 0.510 to 0.652 across ANN models and validation sets. Using MIRS together with MY, FY, PY, and MBW as predictors resulted in improved fitting (r = 0.679-0.777). Including MIRS data improved the weekly average DMI prediction of Canadian Holstein cows, but it seems that MIRS predicts DMI mostly through its association with milk production traits and its utility to estimate a measure of feed efficiency that accounts for the level of production, such as residual feed intake, might be limited and needs further investigation. The better predictive ability of nonlinear ANN compared with linear ANN and partial least squares regression indicated possible nonlinear relationships between weekly average DMI and the predictor variables. In general, ANN using Bayesian regularization and scaled conjugate gradient training algorithms yielded slightly better weekly average DMI predictions compared with ANN using the Levenberg-Marquardt training algorithm

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